Exposure outcome confounder effect modifier Differential vs non differential

Exposure outcome confounder effect modifier

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Exposure, outcome, confounder, effect modifier Differential vs. non-differential Misclassification of exposure: “Differential” is with respect to the outcome Misclassification of outcome: “Differential” is with respect to exposure
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Misclassification of exposure Non-differential You have incorrectly measured exposure, but the extent to which you have done this is the same in those with and without the outcome Example: case-control study, exposure is measured via a biomarker, and there is systematic error in your assay. We don’t expect this to be different in cases versus controls Effect: bias towards the null Differential: Exposure ascertainment results in a different degree of measurement error in those with the outcome versus those without the outcome Example: case-control study, exposure is measured via a biomarker, and the lab has an extra-qualified technician process the case samples Effect: can’t predict, can go all over the place
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Misclassification of outcome Non-differential You have incorrectly measured outcome, but the extent to which you have done this is the same in those with and without the exposure Example: case-control study, and it’s impossible to screen all of the controls to make sure none of them are a case (e.g. this would require invasive surgery) Effect: bias towards the null* Differential: Outcome ascertainment results in a different degree of measurement error in the exposed versus the unexposed Example: cohort study, exposed receive extra scrutiny Effect: can’t predict, can go all over the place *In a follow-up study, measurement of outcome with imperfect sensitivity actually doesn’t produce bias, but it does compromise power
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Misclassification of confounder Always results in residual confounding! Direction of bias will be the same as the direction of bias due to confounding Your adjusted will be between the “crude” and the “truth” We don’t tend to talk about this as differential versus non-differential
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Misclassification Things to “get straight” What are you misclassifying? Differential vs. non-differential Connections to sensitivity and specificity sensitivity <100%: we have falsely classified some people as not having that outcome, exposure, or confounder value, who actually do specificity <100%: we have falsely classified some people as having that value when they do not
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Why does non-differential misclassification bias towards the null? Sensitivity <100%, exposure= obesity, outcome= heart attack: We’re putting a bunch of obese people in the non-obese group If there is an association between obesity and heart attack, we’ll be making the non-obese people look like they have a higher risk of heart attack than they really do Specificity <100%, exposure= obesity, outcome= heart attack: We’re putting a bunch of non-obese people in the obese group If there is an association, we’ll be making the obese people look like they have a lower risk of heart attack than they really do
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  • Fall '16
  • Type I and type II errors, Confounding, Case-control study

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